Ideological roadblocks to humanizing dentistry, an evaluative case study of a continuing education course on social determinants of health
Bibliographic record
Abstract
BACKGROUND: Front line providers of care are frequently lacking in knowledge on and sensitivity to social and structural determinants of underprivileged patients' health. Developing and evaluating approaches to raising health professional awareness and capacity to respond to social determinants is a crucial step in addressing this issue. McGill University, in partnership with Université de Montréal, Québec dental regulatory authorities, and the Québec anti-poverty coalition, co-developed a continuing education (CE) intervention that aims to transfer knowledge and improve the practices of oral health professionals with people living on welfare. Through the use of original educational tools integrating patient narratives and a short film, the onsite course aims to elicit affective learning and critical reflection on practices, as well as provide staff coaching. METHODS: A qualitative case study was conducted, in Montreal Canada, among members of a dental team who participated in this innovative CE course over a period of four months. Data collection consisted in a series of semi-structured individual interviews conducted with 15 members of the dental team throughout the training, digitally recorded group discussions linked to the CE activities, clinic administrative documents and researcher-trainer field notes and journal. In line with adult transformative learning theory, interpretive analysis aimed to reveal learning processes, perceived outcomes and collective perspectives that constrain individual and organizational change. RESULTS: The findings presented in this article consist in four interactive themes, reflective of clinic culture and context, that act as barriers to humanizing patient care: 1) belief in the "ineluctable" commoditization of dentistry; 2) "equal treatment", a belief constraining concern for equity and the recognition of discriminatory practices; 3) a predominantly biomedical orientation to care; and 4) stereotypical categorization of publically insured patients into "deserving" vs. "non-deserving" poor. We discuss implications for oral health policy, orientations for dental education, as well as the role dental regulatory authorities should play in addressing discrimination and prejudice. CONCLUSION: Humanizing care and developing oral health practitioners' capacity to respond to social determinants of health, are challenged by significant ideological roadblocks. These require multi-level and multi-sectorial action if gains in social equity in oral health are to be made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".